OpenRaiser/Intern-Atlas huggingface dataset:
IoT Security and Safety Testing Toolkits for Water Distribution Systems
Venue: arXiv cs.CR 2021
Year: 2021

Due to the critical importance of Industrial Control Systems (ICS) to the operations of cities and countries, research into the security of critical infrastructure has become increasingly relevant and necessary. As a component of both the research and application sides of smart city development, accurate and precise modeling, simulation, and verification are key parts of a robust design and development tools that provide critical assistance in the prevention, detection, and recovery from abnormal behavior in the sensors, controllers, and actuators which make up a modern ICS system. However, while these tools have potential, there is currently a need for helper-tools to assist with their setup and configuration, if they are to be utilized widely. Existing state-of-the-art tools are often technically complex and difficult to customize for any given IoT/ICS processes. This is a serious barrier to entry for most technicians, engineers, researchers, and smart city planners, while slowing down the critical aspects of safety and security verification. To remedy this issue, we take a case study of existing simulation toolkits within the field of water management and expand on existing tools and algorithms with simplistic automated retrieval functionality using a much more in-depth and usable customization interface to accelerate simulation scenario design and implementation, allowing for customization of the cyber-physical network infrastructure and cyber attack scenarios. We additionally provide a novel in-tool-assessment of network's resilience according to graph theory path diversity. Further, we lay out a roadmap for future development and application of the proposed tool, including expansions on resiliency and potential vulnerability model checking, and discuss applications of our work to other fields relevant to the design and operation of smart cities.
FirmwareDroid: Security Analysis of the Android Firmware EcoSystem
Venue: arXiv cs.CR 2021
Year: 2021

The Android Open Source Project (AOSP) is probably the most used and customized operating system for smartphones and IoT devices worldwide. Its market share and high adaptability makes Android an interesting operating system for many developers. Nowadays, we use Android firmware in smartphones, TVs, smartwatches, cars, and other devices by various vendors and manufacturers. The sheer amount of customized Android firmware and devices makes it hard for security analysts to detect potentially harmful applications. Another fact is that many vendors include apps from 3rd party developers. Such bloatware usually has more privileges than standard apps and cannot be removed by the user without rooting the device. In recent years several cases were reported where 3rd party developers could include malicious apps into the Android built chain. Media reports claim that pre-installed malware like Chamois and Triade we able to infect several million devices. Such cases demonstrate the need for better strategies for analyzing Android firmware. In our study, we analyze the Android firmware eco-system in various ways. We collected a dataset with several thousand Android firmware archives and show that several terabytes of firmware data are waiting on the web to be analyzed. We develop a web service called FirmwareDroid for analyzing Android firmware archives and pre-installed apps and create a dataset of firmware samples. Focusing on Android apps, we automated the process of extracting and scanning pre-installed apps with state of the art open-source tools. We demonstrate on real data that pre-installed apps are, in fact, a a threat to Android's users, and we can detect several hundred malware samples using scanners like VirusTotal, AndroGuard, and APKiD. With state of the art tools, we could scan more than 900000 apps during our research and give unique insights into Android custom ROMs.
Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis
Venue: arXiv cs.CL 2021
Year: 2021

We present the first parsing results on the Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME), a 1.9 million word treebank that is an important resource for research in syntactic change. We describe key features of PPCEME that make it challenging for parsing, including a larger and more varied set of function tags than in the Penn Treebank. We present results for this corpus using a modified version of the Berkeley Neural Parser and the approach to function tag recovery of Gabbard et al (2006). Despite its simplicity, this approach works surprisingly well, suggesting it is possible to recover the original structure with sufficient accuracy to support linguistic applications (e.g., searching for syntactic structures of interest). However, for a subset of function tags (e.g., the tag indicating direct speech), additional work is needed, and we discuss some further limits of this approach. The resulting parser will be used to parse Early English Books Online, a 1.1 billion word corpus whose utility for the study of syntactic change will be greatly increased with the addition of accurate parse trees.
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Venue: arXiv cs.LG 2021
Year: 2021

Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as node classification and graph property prediction. Nonetheless, existing systems are inefficient to train large graphs with billions of nodes and edges with GPUs. The main bottlenecks are the process of preparing data for GPUs - subgraph sampling and feature retrieving. This paper proposes BGL, a distributed GNN training system designed to address the bottlenecks with a few key ideas. First, we propose a dynamic cache engine to minimize feature retrieving traffic. By a co-design of caching policy and the order of sampling, we find a sweet spot of low overhead and high cache hit ratio. Second, we improve the graph partition algorithm to reduce cross-partition communication during subgraph sampling. Finally, careful resource isolation reduces contention between different data preprocessing stages. Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20.68x on average.
A Deep Learning Approach for Ontology Enrichment from Unstructured Text
Venue: arXiv cs.CL 2021
Year: 2021

Information Security in the cyber world is a major cause for concern, with a significant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on the web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases, and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded vulnerabilities, threats, controls, products, and other security-related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is deployed to enrich ISO 27001-based information security ontology. The model is trained and tested on a high-performance computing (HPC) environment to handle Wiki text dimensionality. The approach yielded a test accuracy of over 80% when tested with knocked-out concepts from ontology and web page instances to validate the robustness.
EmotionBox: a music-element-driven emotional music generation system using Recurrent Neural Network
Venue: arXiv cs.SD 2021
Year: 2021

With the development of deep neural networks, automatic music composition has made great progress. Although emotional music can evoke listeners' different emotions and it is important for artistic expression, only few researches have focused on generating emotional music. This paper presents EmotionBox -an music-element-driven emotional music generator that is capable of composing music given a specific emotion, where this model does not require a music dataset labeled with emotions. Instead, pitch histogram and note density are extracted as features that represent mode and tempo respectively to control music emotions. The subjective listening tests show that the Emotionbox has a more competitive and balanced performance in arousing a specified emotion than the emotion-label-based method.
Rail Vehicle Localization and Mapping with LiDAR-Vision-Inertial-GNSS Fusion
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we present a global navigation satellite system (GNSS) aided LiDAR-visual-inertial scheme, RailLoMer-V, for accurate and robust rail vehicle localization and mapping. RailLoMer-V is formulated atop a factor graph and consists of two subsystems: an odometer assisted LiDAR-inertial system (OLIS) and an odometer integrated Visual-inertial system (OVIS). Both the subsystem exploits the typical geometry structure on the railroads. The plane constraints from extracted rail tracks are used to complement the rotation and vertical errors in OLIS. Besides, the line features and vanishing points are leveraged to constrain rotation drifts in OVIS. The proposed framework is extensively evaluated on datasets over 800 km, gathered for more than a year on both general-speed and high-speed railways, day and night. Taking advantage of the tightly-coupled integration of all measurements from individual sensors, our framework is accurate to long-during tasks and robust enough to grievously degenerated scenarios (railway tunnels). In addition, the real-time performance can be achieved with an onboard computer.
Can Multilinguality benefit Non-autoregressive Machine Translation?
Venue: arXiv cs.CL 2021
Year: 2021

Non-autoregressive (NAR) machine translation has recently achieved significant improvements, and now outperforms autoregressive (AR) models on some benchmarks, providing an efficient alternative to AR inference. However, while AR translation is often implemented using multilingual models that benefit from transfer between languages and from improved serving efficiency, multilingual NAR models remain relatively unexplored. Taking Connectionist Temporal Classification (CTC) as an example NAR model and Imputer as a semi-NAR model, we present a comprehensive empirical study of multilingual NAR. We test its capabilities with respect to positive transfer between related languages and negative transfer under capacity constraints. As NAR models require distilled training sets, we carefully study the impact of bilingual versus multilingual teachers. Finally, we fit a scaling law for multilingual NAR, which quantifies its performance relative to the AR model as model scale increases.
CLICKER: A Computational LInguistics Classification Scheme for Educational Resources
Venue: arXiv cs.CL 2021
Year: 2021

A classification scheme of a scientific subject gives an overview of its body of knowledge. It can also be used to facilitate access to research articles and other materials related to the subject. For example, the ACM Computing Classification System (CCS) is used in the ACM Digital Library search interface and also for indexing computer science papers. We observed that a comprehensive classification system like CCS or Mathematics Subject Classification (MSC) does not exist for Computational Linguistics (CL) and Natural Language Processing (NLP). We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject. The currently proposed taxonomy includes 334 topics and focuses on educational aspects of CL/NLP; it is based primarily, but not exclusively, on lecture notes from NLP courses. We discuss how such a taxonomy can help in various real-world applications, including tutoring platforms, resource retrieval, resource recommendation, prerequisite chain learning, and survey generation.
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
Venue: arXiv cs.AI 2021
Year: 2021

Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for the development of KGs. Knowledge Graph Embeddings (KGEs) embedding entities and relations of a KG into continuous vector spaces, have been proposed for these reasoning tasks and proven to be efficient and robust. But the plausibility and feasibility of applying and deploying KGEs in real-work applications has not been well-explored. In this paper, we discuss and report our experiences of deploying KGEs in a real domain application: e-commerce. We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems. While non existing KGE could meet all these desiderata, we propose a novel one, an explainable knowledge graph attention network that make prediction through modeling correlations between triples rather than purely relying on its head entity, relation and tail entity embeddings. It could automatically selects attentive triples for prediction and records the contribution of them at the same time, from which explanations could be easily provided and transferable rules could be efficiently produced. We empirically show that our method is capable of meeting all three desiderata in our e-commerce application and outperform typical baselines on datasets from real domain applications.
Reprogrammable Surfaces Through Star Graph Metamaterials
Venue: arXiv cs.RO 2021
Year: 2021

The ability to change a surface's profile allows biological systems to effectively manipulate and blend into their surroundings. Current surface morphing techniques rely either on having a small number of fixed states or on directly driving the entire system. We discovered a subset of scale-independent auxetic metamaterials have a state trajectory with a star-graph structure. At the central node, small nudges can move the material between trajectories, allowing us to locally shift Poisson's ratio, causing the material to take on different shapes under loading. While the number of possible shapes grows exponentially with the size of the material, the probability of finding one at random is vanishingly small. By actively guiding the material through the node points, we produce a reprogrammable surface that does not require inputs to maintain shape and can display arbitrary 2D information and take on complex 3D shapes. Our work opens new opportunities in micro devices, tactile displays, manufacturing, and robotic systems.
Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model
Venue: arXiv cs.LG 2021
Year: 2021

Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor. The experimental results show that the hybrid model is superior to the CNN and MLP models operating separately, and can deliver a high detection accuracy of 99,6% for the bearing faults compared to 98% for CNN and 81% for MLP models.
Asymptotically Optimal Golay-ZCZ Sequence Sets with Flexible Length
Venue: arXiv cs.IT 2021
Year: 2021

Zero correlation zone (ZCZ) sequences and Golay complementary sequences are two kinds of sequences with different preferable correlation properties. Golay-ZCZ sequences are special kinds of complementary sequences which also possess a large ZCZ and are good candidates for pilots in OFDM systems. Known Golay-ZCZ sequences reported in the literature have a limitation in the length which is the form of a power of 2. In this paper, we propose two constructions of Golay-ZCZ sequence sets with new parameters which generalize the constructions of Gong et al. (IEEE Transaction on Communications 61(9), 2013) and Chen et al (IEEE Transaction on Communications 61(9), 2018). Notably, one of the constructions results in optimal binary Golay-ZCZ sequences, while the other results in asymptotically optimal polyphase Golay-ZCZ sequences as the number of sequences increases.
GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional vector cannot fully represent the diversity of user interests. However, most existing models only intercept users' recent interaction behaviors as training data, discarding a large amount of historical interaction sequences. This may raise two issues. On the one hand, data reflecting multiple interests of users is missing; on the other hand, the co-occurrence between items in historical user-item interactions is not fully explored. To tackle the two issues, this paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)". Specifically, a global context extraction module is firstly proposed without introducing any external information, which calculates a weighted co-occurrence matrix based on the constrained co-occurrence number of each item pair and their time interval from the historical interaction sequences of all users and then obtains the global context embedding of each item by using a simplified graph convolution. Secondly, the time interval of each item pair in the recent interaction sequence of each user is captured and combined with the global context item embedding to get the personalized item embedding. Finally, a self-attention based multi-interest framework is applied to learn the diverse interests of users for sequential recommendation. Extensive experiments on the three real-world datasets of Amazon-Books, Taobao-Buy and Amazon-Hybrid show that the performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods. Moreover, the proposed global context extraction module can be easily transplanted to most sequential recommendation models.
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce
Venue: arXiv cs.AI 2021
Year: 2021

We describe a novel decision-making problem developed in response to the demands of retail electronic commerce (e-commerce). While working with logistics and retail industry business collaborators, we found that the cost of delivery of products from the most opportune node in the supply chain (a quantity called the cost-to-serve or CTS) is a key challenge. The large scale, high stochasticity, and large geographical spread of e-commerce supply chains make this setting ideal for a carefully designed data-driven decision-making algorithm. In this preliminary work, we focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to multiple customers in each time period. We compare the relative performance and computational efficiency of several baselines, including heuristics and mixed-integer linear programming. We show that a reinforcement learning based algorithm is competitive with these policies, with the potential of efficient scale-up in the real world.
Khmer Word Search: Challenges, Solutions, and Semantic-Aware Search
Venue: arXiv cs.CL 2021
Year: 2021

Search is one of the key functionalities in digital platforms and applications such as an electronic dictionary, a search engine, and an e-commerce platform. While the search function in some languages is trivial, Khmer word search is challenging given its complex writing system. Multiple orders of characters and different spelling realizations of words impose a constraint on Khmer word search functionality. Additionally, spelling mistakes are common since robust spellcheckers are not commonly available across the input device platforms. These challenges hinder the use of Khmer language in search-embedded applications. Moreover, due to the absence of WordNet-like lexical databases for Khmer language, it is impossible to establish semantic relation between words, enabling semantic search. In this paper, we propose a set of robust solutions to the above challenges associated with Khmer word search. The proposed solutions include character order normalization, grapheme and phoneme-based spellcheckers, and Khmer word semantic model. The semantic model is based on the word embedding model that is trained on a 30-million-word corpus and is used to capture the semantic similarities between words.
Intelli-Paint: Towards Developing Human-like Painting Agents
Venue: arXiv cs.CV 2021
Year: 2021

The generation of well-designed artwork is often quite time-consuming and assumes a high degree of proficiency on part of the human painter. In order to facilitate the human painting process, substantial research efforts have been made on teaching machines how to "paint like a human", and then using the trained agent as a painting assistant tool for human users. However, current research in this direction is often reliant on a progressive grid-based division strategy wherein the agent divides the overall image into successively finer grids, and then proceeds to paint each of them in parallel. This inevitably leads to artificial painting sequences which are not easily intelligible to human users. To address this, we propose a novel painting approach which learns to generate output canvases while exhibiting a more human-like painting style. The proposed painting pipeline Intelli-Paint consists of 1) a progressive layering strategy which allows the agent to first paint a natural background scene representation before adding in each of the foreground objects in a progressive fashion. 2) We also introduce a novel sequential brushstroke guidance strategy which helps the painting agent to shift its attention between different image regions in a semantic-aware manner. 3) Finally, we propose a brushstroke regularization strategy which allows for ~60-80% reduction in the total number of required brushstrokes without any perceivable differences in the quality of the generated canvases. Through both quantitative and qualitative results, we show that the resulting agents not only show enhanced efficiency in output canvas generation but also exhibit a more natural-looking painting style which would better assist human users express their ideas through digital artwork.